import numpy as np
from common.functions import sigmoid,softmax,cross_entropy_error

class Relu:
    def __init__(self):
        self.mask = None 

    def forward(self,x):
        self.mask = (x <= 0)
        out = x.copy() # 这样处理，当修改out时不会修改x
        out[self.mask] = 0
        return out 
    
    def backward(self,dout):
        dout[self.mask] = 0
        dx = dout 

        return dx 
    
class Sigmoid:
    def __init__(self):
        self.out = None
    def forward(self,x):
        out = sigmoid(x)
        self.out = out 
        return out 
    def backward(self,dout):
        dx = dout*(1.0-self.out)*self.out
        return dx 
    
class Affine:
    def __init__(self,W,b):
        self.W = W
        self.b = b 

        self.x = None
        self.original_x_shape = None 

        self.dW = None
        self.db = None

    def forward(self,x):
        # 对应张量
        self.original_x_shape = x.shape
        x = x.reshape(x.shape[0], -1)
        self.x = x 

        out = np.dot(self.x,self.W) + self.b 
        return out 
    
    def backward(self,dout):
        dx = np.dot(dout,self.W.T)
        self.dW = np.dot(self.x.T,dout)
        self.db = np.sum(dout,axis=0)

        dx = dx.reshape(*self.original_x_shape)
        return dx 
    
class SoftmaxWithLoss:
    def __init__(self):
        self.loss = None
        self.y = None
        self.t = None

    def forward(self,x,t):
        self.t = t 
        self.y = softmax(x)
        self.loss = cross_entropy_error(self.y,self.t)

        return self.loss
    
    def backward(self,dout=1):
        batch_size = self.t.shape[0]
        if self.t.size == self.y.size: 
            dx = (self.y-self.t)/batch_size
        else:
            dx = self.y.copy()
            dx[np.arange(batch_size),self.t] -= 1
            dx = dx/batch_size
        return dx 

class Dropout:
    '''过拟合'''
    def __init__(self,dropout_ratio=0.5):
        self.dropout_ratio = dropout_ratio
        self.mask = None 

    def forward(self,x,train_flg=True):
        if train_flg:
            self.mask = np.random.rand(*x.shape) > self.dropout_ratio
            return x*self.mask
        else:
            return x*(1.0-self.dropout_ratio)
        
    def backward(self,dout):
        return dout*self.mask
    
class BatchNormalization:
    '''批量归一化算法'''
    def __init__(self,gamma,beta,momentum=0.9,running_mean=None,running_var=None):
        self.gamma = gamma
        self.beta = beta
        self.momentum = momentum
        self.input_shape = None 

        # 测试时使用的平均值和方差
        self.running_mean = running_mean
        self.running_var = running_var

        # backward时使用的中间数据
        self.batch_size = None 
        self.xc = None 
        self.std = None 
        self.dgamma = None 
        self.dbeta = None 

    def forward(self,x,train_flg=True):
        self.input_shape = x.shape
        if x.ndim != 2:
            N,C,H,W = x.shape
            x = x.reshape(N,-1)
        out = self.__forward(x,train_flg)

        return out.reshape(*self.input_shape)  
    
    def __forward(self,x,train_flg):
        if self.running_mean is None:
            N,D = x.shape
            self.running_mean = np.zeros(D)
            self.running_var = np.zeros(D)

        if train_flg:
            mu = x.mean(axis=0)
            xc = x - mu 
            var = np.mean(xc**2,axis=0)
            std = np.sqrt(var+10e-7)
            xn = xc/std 

            self.batch_size = x.shape[0]
            self.xc = xc 
            self.xn = xn
            self.std = std 
            self.running_mean = self.momentum*self.running_mean + (1-self.momentum)*mu 
            self.running_var = self.momentum*self.running_var + (1-self.momentum)*var 
        else:
            xc = x - self.running_mean
            xn = xc/((np.sqrt(self.running_var+10e-7)))

        out = self.gamma*xn + self.beta
        return out 
    
    def backward(self,dout):
        if dout.ndim != 2:
            N,C,H,W = dout.shape
            dout = dout.reshape(N,-1)
        
        dx = self.__backward(dout)

        dx = dx.reshape(*self.input_shape)
        return dx 
    
    def __backward(self,dout):
        dbeta = dout.sum(axis=0)
        dgamma = np.sum(self.xn*dout, axis=0)
        dxn = self.gamma*dout 
        dxc = dxn/self.std 
        dstd = -np.sum((dxn*self.xc)/(self.std*self.std),axis=0)
        dvar = 0.5*dstd/self.std
        dxc += (2.0/self.batch_size)*self.xc*dvar 
        dmu = np.sum(dxc,axis=0)
        dx = dxc-dmu/self.batch_size

        self.dgamma = dgamma
        self.dbeta = dbeta
        
        return dx 
    
if __name__ == '__main__':
    x = np.array([[1.0,-0.5],[-2.0,3.0]])
    relu = Relu()
    ret = relu.forward(x)
    print(ret)
